Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program
arXiv:2604.16450v2 Announce Type: replace-cross Abstract: Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess demographic attributes independently. FairLogue, a toolkit for intersectional fairness auditing, was applied across multiple clinical prediction tasks to evaluate disparities across combined demographic groups. Using the All of Us dataset, two published models were selected for replication and evaluation: (A) prediction of selective serotonin reuptake inhibitor associated bleeding events and
The proliferation of AI in healthcare, coupled with increasing scrutiny on ethical AI, makes intersectional fairness a critical and timely concern.
This research highlights the necessity of addressing biases in clinical AI models to prevent exacerbating health disparities and ensure equitable outcomes for diverse patient populations.
Fairness evaluations in clinical AI are moving beyond single demographic attributes to a more complex, intersectional approach, demanding more robust auditing tools and practices.
- · Healthcare AI ethics researchers
- · Patients from underrepresented groups
- · Developers of fairness auditing tools
- · Healthcare providers seeking equitable AI solutions
- · AI models with unaddressed intersectional biases
- · Healthcare systems relying on biased AI without scrutiny
- · Developers ignoring rigorous fairness testing
Increased demand for robust fairness auditing tools and methodologies in clinical AI development.
New regulatory guidelines or industry standards emerging to mandate intersectional fairness evaluations for healthcare AI.
Enhanced trust in AI-driven clinical diagnoses and treatments, leading to broader adoption and better health outcomes for a wider population.
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Read at arXiv cs.LG